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#include "precomp.hpp"

typedef struct CvDI {
	double d;
	int    i;
} CvDI;

int CV_CDECL
icvCmpDI( const void* a, const void* b, void* ) {
	const CvDI* e1 = (const CvDI*) a;
	const CvDI* e2 = (const CvDI*) b;

	return (e1->d < e2->d) ? -1 : (e1->d > e2->d);
}

CV_IMPL void
cvCreateTestSet( int type, CvMat** samples,
				 int num_samples,
				 int num_features,
				 CvMat** responses,
				 int num_classes, ... ) {
	CvMat* mean = NULL;
	CvMat* cov = NULL;
	CvMemStorage* storage = NULL;

	CV_FUNCNAME( "cvCreateTestSet" );

	__BEGIN__;

	if ( samples ) {
		*samples = NULL;
	}
	if ( responses ) {
		*responses = NULL;
	}

	if ( type != CV_TS_CONCENTRIC_SPHERES ) {
		CV_ERROR( CV_StsBadArg, "Invalid type parameter" );
	}

	if ( !samples ) {
		CV_ERROR( CV_StsNullPtr, "samples parameter must be not NULL" );
	}

	if ( !responses ) {
		CV_ERROR( CV_StsNullPtr, "responses parameter must be not NULL" );
	}

	if ( num_samples < 1 ) {
		CV_ERROR( CV_StsBadArg, "num_samples parameter must be positive" );
	}

	if ( num_features < 1 ) {
		CV_ERROR( CV_StsBadArg, "num_features parameter must be positive" );
	}

	if ( num_classes < 1 ) {
		CV_ERROR( CV_StsBadArg, "num_classes parameter must be positive" );
	}

	if ( type == CV_TS_CONCENTRIC_SPHERES ) {
		CvSeqWriter writer;
		CvSeqReader reader;
		CvMat sample;
		CvDI elem;
		CvSeq* seq = NULL;
		int i, cur_class;

		CV_CALL( *samples = cvCreateMat( num_samples, num_features, CV_32FC1 ) );
		CV_CALL( *responses = cvCreateMat( 1, num_samples, CV_32SC1 ) );
		CV_CALL( mean = cvCreateMat( 1, num_features, CV_32FC1 ) );
		CV_CALL( cvSetZero( mean ) );
		CV_CALL( cov = cvCreateMat( num_features, num_features, CV_32FC1 ) );
		CV_CALL( cvSetIdentity( cov ) );

		/* fill the feature values matrix with random numbers drawn from standard
		   normal distribution */
		CV_CALL( cvRandMVNormal( mean, cov, *samples ) );

		/* calculate distances from the origin to the samples and put them
		   into the sequence along with indices */
		CV_CALL( storage = cvCreateMemStorage() );
		CV_CALL( cvStartWriteSeq( 0, sizeof( CvSeq ), sizeof( CvDI ), storage, &writer ));
		for ( i = 0; i < (*samples)->rows; ++i ) {
			CV_CALL( cvGetRow( *samples, &sample, i ));
			elem.i = i;
			CV_CALL( elem.d = cvNorm( &sample, NULL, CV_L2 ));
			CV_WRITE_SEQ_ELEM( elem, writer );
		}
		CV_CALL( seq = cvEndWriteSeq( &writer ) );

		/* sort the sequence in a distance ascending order */
		CV_CALL( cvSeqSort( seq, icvCmpDI, NULL ) );

		/* assign class labels */
		num_classes = MIN( num_samples, num_classes );
		CV_CALL( cvStartReadSeq( seq, &reader ) );
		CV_READ_SEQ_ELEM( elem, reader );
		for ( i = 0, cur_class = 0; i < num_samples; ++cur_class ) {
			int last_idx;
			double max_dst;

			last_idx = num_samples * (cur_class + 1) / num_classes - 1;
			CV_CALL( max_dst = (*((CvDI*) cvGetSeqElem( seq, last_idx ))).d );
			max_dst = MAX( max_dst, elem.d );

			for ( ; elem.d <= max_dst && i < num_samples; ++i ) {
				CV_MAT_ELEM( **responses, int, 0, elem.i ) = cur_class;
				if ( i < num_samples - 1 ) {
					CV_READ_SEQ_ELEM( elem, reader );
				}
			}
		}
	}

	__END__;

	if ( cvGetErrStatus() < 0 ) {
		if ( samples ) {
			cvReleaseMat( samples );
		}
		if ( responses ) {
			cvReleaseMat( responses );
		}
	}
	cvReleaseMat( &mean );
	cvReleaseMat( &cov );
	cvReleaseMemStorage( &storage );
}

/* End of file. */
